A new population pharmacokinetic model for vancomycin in patients with variable renal function: Therapeutic drug monitoring based on extended covariate model using CKD-EPI estimation

Evaluation of Vancomycin Dose Needed to Achieve 24-Hour Area Under the Concentration-Time Curve to Minimum Inhibitory Concentration Ratio Greater Than or Equal to 400 Using Pharmacometric Approaches in Pediatric Intensive Care Patients

OBJECTIVES

To investigate which independent factor(s) have an impact on the pharmacokinetics of vancomycin in critically ill children, develop an equation to predict the 24-hour area under the concentration-time curve from a trough concentration, and evaluate dosing regimens likely to achieve a 24-hour area under the concentration-time curve to minimum inhibitory concentration ratio (AUC24/MIC) greater than or equal to 400.

DESIGN

Prospective population pharmacokinetic study of vancomycin.

SETTING

Critically ill patients in quaternary care PICUs.

PATIENTS

Children 90 days old or older to younger than 18 years who received IV vancomycin treatment, irrespective of the indication for use, in the ICUs at the University of Maryland Children's Hospital and Texas Children's Hospital were enrolled.

INTERVENTIONS

Vancomycin was prescribed at doses and intervals chosen by the treating clinicians.

MEASUREMENTS AND MAIN RESULTS

A median of four serum levels of vancomycin per patient were collected along with other variables for up to 7 days following the first administration. These data were used to characterize vancomycin pharmacokinetics and evaluate the factors affecting the variability in achieving AUC24/MIC ratio greater than or equal to 400 in PICU patients who are not on extracorporeal therapy. A total of 302 children with a median age of 6.0 years were enrolled. A two-compartment model described the pharmacokinetics of vancomycin with the clearance of 2.76 L/hr for a typical patient weighing 20 kg. The glomerular filtration rate estimated using either the bedside Schwartz equation or the chronic kidney disease in children equation was the only statistically significant predictor of clearance among the variables evaluated, exhibiting equal predictive performance. The trough levels achieving AUC24/MIC = 400 were 5.6-10.0 μg/mL when MIC = 1 μg/mL. The target of AUC24/MIC greater than or equal to 400 was achieved in 60.4% and 36.5% with the typical dosing regimens of 15 mg/kg every 6 and 8 hours (q6h and q8h), respectively.

CONCLUSIONS

The pharmacokinetics of vancomycin in critically ill children were dependent on the estimated glomerular filtration rate only. Trough concentrations accurately predict AUC24. Typical pediatric vancomycin dosing regimens of 15 mg/kg q6h and q8h will often lead to AUC24/MIC under 400.

Optimize Vancomycin Dose in Surgical Ward Patients with Augmented Renal Clearance Determined by Chronic Kidney Disease Epidemiology Collaboration Equation

Background

In the field of postoperative care, infections caused by Gram-positive bacteria pose a major clinical challenge. Vancomycin is a key therapeutic agent whose efficacy is greatly influenced by renal function, particularly by augmented renal clearance (ARC). The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) estimated glomerular filtration rate (eGFR) is an easy and commonly used method to predict ARC; however, it is not well studied to determine vancomycin dose. In this study, we examined the effectiveness of the CKD-EPI equation in determining ARC and optimizing the dose of vancomycin for surgical ward patients.

Methodology

A retrospective observational study was conducted to examine 158 surgical ward patients receiving vancomycin. Data on demographics, medical history, and vancomycin dosing were collected. Renal function was evaluated using the CKD-EPI equation, with ARC defined as eGFR ≥ 96.5 mL/min/1.73 m2. Vancomycin pharmacokinetics were calculated using the ClinCalc tool.

Results

ARC was in 54% of the patients. Compared with patients without ARC, those with ARC were younger and had lower serum creatinine levels. They also required higher vancomycin doses but had lower trough concentrations and 24-hour area-under-the-curve values. A significant correlation was observed between eGFR and vancomycin clearance, with eGFR > 96.5 mL/min/1.73 m2 necessitating higher vancomycin doses (>45 mg/kg/day) to achieve the desired area under the curve to minimum inhibitory concentration ratio.

Conclusion

For surgical ward patients with CKD-EPI eGFR ≥ 96.5 mL/min/1.73 m2, a vancomycin dosage of >45 mg/kg/day may be recommended to reach effective therapeutic levels. Overall, this study emphasizes the importance of tailoring vancomycin therapy depending on renal function to ensure efficacy and mitigate the risk of antimicrobial resistance in surgical ward patients.

Vancomycin: An analysis and evaluation of eight population pharmacokinetic models for clinical application in general adult population

INTRODUCTION

Based on the recent guidelines for vancomycin therapeutic drug monitoring (TDM), the area under the curve to minimum inhibitory concentration ratio was to be employed combined with the usage of population pharmacokinetic (popPK) model for dosing adaptation. Yet, deploying these models in a clinical setting requires an external evaluation of their performance.

OBJECTIVES

This study aimed to evaluate existing vancomycin popPK models from the literature for the use in TDM within the general patient population in a clinical setting.

METHODS

The models under external evaluation were chosen based on a review of literature covering vancomycin popPK models developed in general adult populations. Patients' data were collected from Charles-Le Moyne Hospital (CLMH). The external evaluation was performed with NONMEM® (v7.5). Additional analyses such as evaluating the impact of number of samples on external evaluation, Bayesian forecasting, and a priori dosing regimen simulations were performed on the best performing model.

RESULTS

Eight popPK models were evaluated with an independent dataset that included 40 patients and 252 samples. The model developed by Goti and colleagues demonstrated superior performance in diagnostic plots and population predictive performance, with bias and inaccuracy values of 0.251% and 22.7%, respectively, and for individual predictive performance, bias and inaccuracy were -4.90% and 12.1%, respectively. When limiting the independent dataset to one or two samples per patient, the Goti model exhibited inadequate predictive performance for inaccuracy, with values exceeding 30%. Moreover, the Goti model is suitable for Bayesian forecasting with at least two samples as prior for the prediction of the next trough concentration. Furthermore, the vancomycin dosing regimen that would maximize therapeutic targets of area under the curve to minimum inhibitory concentration ratio (AUC24/MIC) and trough concentrations (Ctrough) for the Goti model was 20 mg/kg/dose twice daily.

CONCLUSION

Considering the superior predictive performance and potential for Bayesian forecasting in the Goti model, future research aims to test its applicability in clinical settings at CLMH, both in a priori and a posteriori scenario.

Population Pharmacokinetics and Model-Based Dose Optimization of Vancomycin in Sudanese Adult Patients with Renal Impairment

Purpose

The study aimed to perform a population pharmacokinetic (PK) analysis to obtain vancomycin PK parameter estimates in Sudanese adult patients. The population PK model is then applied to perform model-based dose optimization.

Patients and Methods

Data were collected through a retrospective, single-center, observational cohort study performed in Aliaa Specialist Hospital, Khartoum, Sudan. A population PK model was developed using the MonolixSuite 2020R1 to explore the potential effects of demographics and laboratory covariates on vancomycin PK. Monte Carlo simulations were performed to optimize dosage regimens as a function of creatinine clearance (CLcr) and virtual patients were partitioned into five CLcr groups.

Results

We retrospectively collected 194 vancomycin plasma concentrations from 99 adults. The median (interquartile range) for age (years) and CLcr (mL/min) were 65 (50-75) and 12.7 (5.52-25.78), respectively. Vancomycin PK data were best fitted using a one-compartment model with linear elimination. The estimates of clearance and volume of distribution were 2.02 L/h and 65 L, respectively. CLcr was identified as the main covariate explaining the PK variability in vancomycin CL. CL significantly decreased with decreasing CLcr. For the five CLcr groups evaluated, a tailored vancomycin daily maintenance dose (using patients' CLcr) ranged from 200 to 1650 mg. Overall, simulations showed that 45% (CI; 41.11-47.36%) of patients would achieve a target AUC with the suggested dosages.

Conclusion

A population PK model of vancomycin was developed using data obtained from adult Sudanese patients. Model-based dose optimization can aid clinicians in selecting initial vancomycin doses that will maximize the likelihood of a favorable treatment response.

How to Dose Vancomycin in Overweight and Obese Patients with Varying Renal (Dys)function in the Novel Era of AUC 400-600 mg·h/L-Targeted Dosing

BACKGROUND AND OBJECTIVE

The latest vancomycin guideline recommends area under the curve (AUC)-targeted dosing and monitoring for efficacy and safety. However, guidelines for AUC-targeted starting dosing in patients with obesity and/or renal insufficiency are currently lacking. This study quantifies the pharmacokinetics (PK) of vancomycin in this population and provides AUC-targeted dosing recommendations.

METHODS

Vancomycin concentrations (n = 1188) from therapeutic drug monitoring of 210 overweight and obese patients with varying degrees of renal (dys)function from the ward (74.8%) and intensive care unit (ICU, 25.2%) were pooled with published rich concentration-time data (n = 207) from 20 (morbidly) obese subjects undergoing bariatric surgery. A population model was developed using NONMEM 7.4. Stochastic simulations were performed to design dosing guidelines targeting an AUC24 between 400-600 mg·h/L.

RESULTS

Vancomycin clearance (CL) was found to increase linearly with total bodyweight and with renal function (CKD-EPI) in a power relation. Additionally, CL proved 15.5% lower in ICU patients. Our model shows that, to reach the target AUC between 400 and 600 mg·h/L in the first 48 h, two loading doses are required for both continuous infusion and intermittent dosing regimens. Maintenance doses were found to require adjustment for total bodyweight, renal function, and ICU admission status. With this guideline, the median AUC24 is well within the target from the start of the treatment onwards.

CONCLUSIONS

To achieve safe and effective vancomycin exposure for maintenance doses in overweight and obese patients, renal function, total bodyweight, and ICU admission status should be taken into account.

TRIAL REGISTRATION

The AMIGO trial was registered in the Dutch Trial Registry [NTR6058].

Model-informed precision dosing in vancomycin treatment

Introduction: While vancomycin remains a widely prescribed antibiotic, it can cause ototoxicity and nephrotoxicity, both of which are concentration-associated. Overtreatment can occur when the treatment lasts for an unnecessarily long time. Using a model-informed precision dosing scheme, this study aims to develop a population pharmacokinetic (PK) and pharmacodynamic (PD) model for vancomycin to determine the optimal dosage regimen and treatment duration in order to avoid drug-induced toxicity. Methods: The data were obtained from electronic medical records of 542 patients, including 40 children, and were analyzed using NONMEM software. For PK, vancomycin concentrations were described with a two-compartment model incorporating allometry scaling. Results and discussion: This revealed that systemic clearance decreased with creatinine and blood urea nitrogen levels, history of diabetes and renal diseases, and further decreased in women. On the other hand, the central volume of distribution increased with age. For PD, C-reactive protein (CRP) plasma concentrations were described by transit compartments and were found to decrease with the presence of pneumonia. Simulations demonstrated that, given the model informed optimal doses, peak and trough concentrations as well as the area under the concentration-time curve remained within the therapeutic range, even at doses smaller than routine doses, for most patients. Additionally, CRP levels decreased more rapidly with the higher dose starting from 10 days after treatment initiation. The developed R Shiny application efficiently visualized the time courses of vancomycin and CRP concentrations, indicating its applicability in designing optimal treatment schemes simply based on visual inspection.

Individualized Delivery of Vancomycin by Model-Informed Bayesian Dosing Approach to Maintain an AUC24 Target in Critically Ill Patients

INTRODUCTION

Monitoring of AUC24 was updated recommendation in the guideline for the therapeutic drug monitoring (TDM) of vancomycin in Chinese pharmacological society published in 2020. Vancomycin pharmacokinetic profiles are diverse and unique in critically ill patients because of the drastic variability of the patients' physiological parameters, while the study for population pharmacokinetic (PPK) models in Chinese critically ill patients has been rarely reported. The objectives of this study were to construct a PPK model to describe the pharmacokinetic characteristics of vancomycin in critically ill patients and to individualize vancomycin dosing by model-informed Bayesian estimation for maintenance of AUC24 target at 400-650 mg h/L recommended by the 2020 guideline.

METHODS

Vancomycin with different dosing was administered intravenously over 1 h for critically ill patients, TDM was started at 48 h or 72 h since initiation of vancomycin therapy for patients. Blood samples were collected from patients for trough concentrations or Cmax. Vancomycin concentrations were determined by high-performance liquid chromatography method with ultraviolet detection. PPK model was performed using the nonlinear mixed-effect model (NONMEM®). Individual PK parameters for critically ill patients treated with vancomycin were estimated using a post hoc empirical Bayesian method based on the final PPK model. AUC24 was calculated as the total daily dose divided by the clearance (L/h).

RESULTS

The PPK of vancomycin was determined by a one-compartment model with creatinine clearance as fixed effects. The PK estimates in the final model generally agreed with the median estimates and were contained within the 95% CI generated from the bootstrap results, indicating good precision and stability in the final model. The visual predictive check plots showed the adequate predictive performance of the final PK model and supported a good model fit. The model-informed Bayesian estimation was used to predict the AUC24 of critically ill patient by the acquired TDM results, and the dosing adjustment by maintenance of AUC24 at 400-650 mg h/L had made a great therapeutic effect for the case.

CONCLUSION

This study established a PPK model of vancomycin in Chinese critically ill patients, and individualized dosing of vancomycin by model-informed Bayesian estimation to maintain an AUC24 target at 400-650 mg h/L has been successfully applied in clinic. This result supports the continued use of model-informed Bayesian estimation to vancomycin treatment in critically ill patients.

Clinical Relevance of a Vancomycin 24 h Area under the Concentration-Time Curve Values Using Different Renal Function Equations in Bayesian Dosing Software

With the updated 2020 vancomycin therapeutic drug monitoring (TDM) guidelines suggesting a ratio of area under the curve over 24 h to a minimum inhibitory concentration (AUC24/MIC) as a target from the Infectious Diseases Society of America, an accurate estimation of AUC24 has become more critical. We aim to compare the AUC24 using Bayesian dosing software according to various estimated glomerular filtration rate (eGFR) equations in order to analyze the clinical impact of eGFR in vancomycin TDM. We reviewed the TDM dataset of 214 adult patients and analyzed the AUC24 values from various renal function equations, including the Cockcroft-Gault (C-G), the modification of diet in renal disease (MDRD), the chronic kidney disease epidemiology collaboration (CKD-EPI), and the revised Lund−Malmö. The AUC24/MIC results (assuming a MIC of 1 mg/L) were divided into three groups as follows: <400, 400−600, and >600. Additionally, we compared the group agreement between the C-G and the three eGFR formulas. Although there was a statistically significant difference in the AUC24 of the MDRD and the CKD-EPI formulas compared to the C-G, the group concordance rate of the eGFR formula was 95.2−100%, which indicates no clinical significance. The clinical impact of the eGFR formula type on drug dosing recommendations in vancomycin TDM using Bayesian software was insignificant in clinical practice.

Evaluation of vancomycin pharmacokinetics in patients with augmented renal clearances: A randomized clinical trial

Purpose: Vancomycin is a narrow therapeutic window glycopeptide antibiotic that acts against Gram-positive bacteria. As it is renally eliminated, therapeutic drug monitoring is recommended for vancomycin, especially in case of kidney function alteration. Augmented renal clearance (ARC), defined as a creatinine clearance of more than 130 ml/min, is a risk factor for sub-therapeutic concentrations of vancomycin. This study aimed to evaluate the vancomycin pharmacokinetics following the administration of two different regimens in ARC patients.

Methods: A randomized clinical trial (IRCT20180802040665N1) was conducted on patients in need of vancomycin therapy. Eight hours of urine was collected and 56 patients divided into two groups with creatinine clearance of more than 130 ml/min were included in the study. The first group received 15 mg/kg of vancomycin every 12 h and the second group 15 mg/kg every 8 h. After four doses, the peak and trough concentrations were measured from two blood samples. The primary outcome was the percentage of patients who attainted AUC more than 400. The occurrence of acute kidney injury also was evaluated after seven days.

Results: The mean age of patients in the every 12 h and every 8 h groups was 44.04 ± 16.55 and 42.86 ± 11.83 years, respectively. While neurosurgical issues were the most common causes of hospitalization, central nervous infections were the most common indications for vancomycin initiation. Urinary creatinine clearance was 166.94 ± 41.32 ml/min in the every 12 h group and 171.78 ± 48.56 ml/min in the every 8 h group. 46.42% of patients in the every 12 h group and 82.14% of patients in the every 8 h group attained AUC/MIC of more than 400 mg × hr/L. None of the patients in the every 12 h group reached more than 15 mcg/ml concentration. At the 7-day follow-up, 10.7% patients in the BD group and 28.6% patients in the TDS group developed acute kidney injury (p = 0.089).

Conclusion: Administration of vancomycin at a dose of 15 mg/kg every 8 h is associated with higher pharmacokinetic attainment in ARC patients. The occurrence of acute kidney injury also was not significantly higher in this therapeutic regimen. AUC/MIC monitoring is necessary in this population.

Two Innovative Approaches to Optimize Vancomycin Dosing Using Estimated AUC after First Dose: Validation Using Data Generated from Population PK Model Coupled with Monte-Carlo Simulation and Comparison with the First-Order PK Equation Approach

The revised consensus guidelines for optimizing vancomycin doses suggest that maintaining the area under the concentration-time curve to minimal inhibitory concentration ratio (AUC/MIC) of 400–600 mg·h/L is the target pharmacokinetic/pharmacodynamic (PK/PD) index for efficacy. AUC-guided dosing approach uses a first-order pharmacokinetics (PK) equation to estimate AUC using two samples obtained at steady state and one-compartment model, which can cause inaccurate AUC estimation and fail to achieve the effective PK/PD target early in therapy (days 1 and 2). To achieve an efficacy target from the third or fourth dose, two innovative approaches (Method 1 and Method 2) to estimate vancomycin AUC at steady state (AUCSS) using two-compartment model and three or four levels after the first dose are proposed. The feasibility of the proposed methods was evaluated and compared with another published dosing algorithm (Method 3), which uses two samples and a one-compartment approach. Monte Carlo simulation was performed using a well-established population PK model, and concentration-time profiles for virtual patients with various degrees of renal function were generated, with 1000 subjects per group. AUC extrapolated to infinity (AUC0–∞) after the first dose was estimated using the three methods, whereas reference AUC (AUCref) was calculated using the linear-trapezoidal method at steady state after repeated doses. The ratio of AUC0–∞: AUCref and % bias were selected as the indicators to evaluate the accuracy of three methods. Sensitivity analysis was performed to examine the influence of change in each sampling time on the estimated AUC0–∞ using the two proposed approaches. For simulated patients with various creatinine clearance, the mean of AUC0–∞: AUCref obtained from Method 1, Method 2 and Method 3 ranged between 0.98 to 1, 0.96 to 0.99, and 0.44 to 0.69, respectively. The mean bias observed with the three methods was −0.10% to −2.09%, −1.30% to −3.59% and −30.75% to −55.53%, respectively. The largest mean bias observed by changing sampling time while using Method 1 and Method 2 were −4.30% and −10.50%, respectively. Three user-friendly and easy-to-use excel calculators were built based on the two proposed methods. The results showed that our approaches ensured sufficient accuracy and achieved target PK/PD index early and were superior to the published methodologies. Our methodology has the potential to be used for vancomycin dose optimization and can be easily implemented in clinical practice.

Augmented Renal Clearance in Severe Infections-An Important Consideration in Vancomycin Dosing: A Narrative Review

Vancomycin is a hydrophilic antibiotic widely used in severe infections, including bacteremia and central nervous system (CNS) infections caused by Gram-positive bacteria such as methicillin-resistant Staphylococcus aureus (MRSA), coagulase-negative staphylococci and enterococci. Appropriate antimicrobial dosage regimens can help achieve the target exposure and improve clinical outcomes. However, vancomycin exposure in serum and cerebrospinal fluid (CSF) is challenging to predict due to rapidly changing pathophysiological processes and patient-specific factors. Vancomycin concentrations may be decreased for peripheral infections due to augmented renal clearance (ARC) and increased distribution caused by systemic inflammatory response syndrome (SIRS), increased capillary permeability, and aggressive fluid resuscitation. Additionally, few studies on vancomycin’s pharmacokinetics (PK) in CSF for CNS infections. The relationship between exposure and clinical response is unclear, challenging for adequate antimicrobial therapy. Accurate prediction of vancomycin pharmacokinetics/pharmacodynamics (PK/PD) in patients with high interindividual variation is critical to increase the likelihood of achieving therapeutic targets. In this review, we describe the interaction between ARC and vancomycin PK/PD, patient-specific factors that influence the achievement of target exposure, and recent advances in optimizing vancomycin dosing schedules for severe infective patients with ARC.

Pharmacokinetic equations versus Bayesian guided vancomycin monitoring: Pharmacokinetic model and model-informed precision dosing trial simulations

The recently released revised vancomycin consensus guideline endorsed area under the concentration-time curve (AUC) guided monitoring. Means to AUC-guided monitoring include pharmacokinetic (PK) equations and Bayesian software programs, with the latter approach being preferable. We aimed to evaluate the predictive performance of these two methods when monitoring using troughs or peaks and troughs at varying single or mixed dosing intervals (DIs), and evaluate the significance of satisfying underlying assumptions of steady-state and model transferability. Methods included developing a vancomycin population PK model and conducting model-informed precision dosing clinical trial simulations. A one-compartment PK model with linear elimination, exponential between-subject variability, and mixed (additive and proportional) residual error model resulted in the best model fit. Conducted simulations demonstrated that Bayesian-guided AUC can, potentially, outperform that of equation-based AUC predictions depending on the quality of model diagnostics and met assumptions. Ideally, Bayesian-guided AUC predictive performance using a trough from the first DI was equivalent to that of PK equations using two measurements (peak and trough) from the fifth DI. Model transferability diagnostics can guide the selection of Bayesian priors but are not strong indicators of predictive performance. Mixed versus single fourth and/or fifth DI sampling seems indifferent. This study illustrated cases associated with the most reliable AUC predictions and showed that only proper Bayesian-guided monitoring is always faster and more reliable than equations-guided monitoring in pre-steady-state DIs in the absence of a loading dose. This supports rapid Bayesian monitoring using data as sparse and early as a trough at the first DI.

Comparison of area under the curve for vancomycin from one- and two-compartment models using sparse data

BACKGROUND AND OBJECTIVE

Vancomycin pharmacokinetics have been described by both one- and two-compartment models. One-compartment models are widely used to predict the area under the curve (AUC), a useful parameter for determining the efficacy and safety of vancomycin, based on sparse data collected during therapeutic drug monitoring. It is uncertain whether AUCs from one-compartment models with sparsely sampled data can sufficiently represent the true AUC. This study aimed to compare AUC estimates from one- and two-compartment models using sparse data. The reliability of AUCs from models constructed with trough-only data was also assessed.

METHODS

A previously published robust model was used to simulate vancomycin concentration points at 15 min intervals in 100 patients. From these simulated data, the reference AUC (AUC_ref) was calculated and two depleted dataset versions (trough-only and peak-trough datasets) were also created. One- and two-compartment models were built from the depleted datasets with the use of NONMEM. Vancomycin 24-hour AUC was calculated from concentration-time profiles of each model by a linear trapezoidal formula at three different time periods: 0-24 hours (AUC_0-24), 24-48 hours (AUC_24-48) and 0-48 hours (AUC_avg). The deviation of each of the AUCs from the AUC_ref was examined to assess the AUC predictability of models from sparse data. The difference in AUCs between one- and two-compartment models was analysed from statistical and clinical perspectives.

RESULTS

When assessing the deviation of each AUC from the AUC_ref, the one-compartment model from both peak-trough and trough-only data could adequately represent the true AUC with no statistically significant differences. Two-compartment model from peak-trough data also provided similar AUC estimates with the AUCref. However, AUCs from the two-compartment model with trough-only data did not adequately represent the true AUC, with significant differences of 25.16% for AUC_0-24, 15.92% for AUC_24-48 and 19.45% for AUC_avg.

CONCLUSION

Regardless of statistically significant differences between AUCs from one- and two-compartment models, the level of difference was acceptable from the clinical perspective, being <17% in models from peak-trough data. Therefore, both one- and two-compartment models with sparse data having at least a pair of peak-trough data per patient could be reliable for predicting AUC. Furthermore, AUCs of the one-compartment model from trough-only data did not show a significant difference from the AUC_ref. Hence, one-compartment models developed from trough-only data could be useful for predicting AUC when models with rich data are not available for the intended population. However, it is suggested that the use of the two-compartment model built from trough-only data should be avoided.

AI Models to Assist Vancomycin Dosage Titration

Background: Effective treatment using antibiotic vancomycin requires close monitoring of serum drug levels due to its narrow therapeutic index. In the current practice, physicians use various dosing algorithms for dosage titration, but these algorithms reported low success in achieving therapeutic targets. We explored using artificial intelligent to assist vancomycin dosage titration. Methods: We used a novel method to generate the label for each record and only included records with appropriate label data to generate a clean cohort with 2,282 patients and 7,912 injection records. Among them, 64% of patients were used to train two machine learning models, one for initial dose recommendation and another for subsequent dose recommendation. The model performance was evaluated using two metrics: PAR, a pharmacology meaningful metric defined by us, and Mean Absolute Error (MAE), a commonly used regression metric. Results: In our 3-year data, only a small portion (34.1%) of current injection doses could reach the desired vancomycin trough level (14-20 mcg/ml). Both PAR and MAE of our machine learning models were better than the classical pharmacokinetic models. Our model also showed better performance than the other previously developed machine learning models in our test data. Conclusion: We developed machine learning models to recommend vancomycin dosage. Our results show that the new AI-assisted dosage titration approach has the potential to improve the traditional approaches. This is especially useful to guide decision making for inexperienced doctors in making consistent and safe dosing recommendations for high-risk medications like vancomycin.

Dose Tailoring of Vancomycin Through Population Pharmacokinetic Modeling Among Surgical Patients in Pakistan

Background: Vancomycin is a narrow therapeutic agent, and it is necessary to optimize the dose to achieve safe therapeutic outcomes. The purpose of this study was to identify the significant covariates for vancomycin clearance and to optimize the dose among surgical patients in Pakistan. Methods: Plasma concentration data of 176 samples collected from 58 surgical patients treated with vancomycin were used in this study. A population pharmacokinetic model was developed on NONMEM^® using plasma concentration-time data. The effect of all available covariates was evaluated on the pharmacokinetic parameters of vancomycin by stepwise covariate modeling. The final model was evaluated using bootstrap, goodness-of-fit plots, and visual predictive checks. Results: The pharmacokinetics of vancomycin followed a one-compartment model with first-order elimination. The vancomycin clearance (CL) and volume of distribution (Vd) were 2.45 L/h and 22.6 l, respectively. Vancomycin CL was influenced by creatinine clearance (CRCL) and body weight of the patients; however, no covariate was significant for its effect on the volume of distribution. Dose tailoring was performed by simulating dosage regimens at a steady state based on the CRCL of the patients. The tailored doses were 400, 600, 800, and 1,000 mg for patients with a CRCL of 20, 60, 100, and 140 ml/min, respectively. Conclusion: Vancomycin CL is influenced by CRCL and body weight of the patient. This model can be helpful for the dose tailoring of vancomycin based on renal status in Pakistani patients.

Population Pharmacokinetic Modeling of Vancomycin in Thai Patients With Heterogeneous and Unstable Renal Function

BACKGROUND

Vancomycin is widely used to treat gram-positive bacterial infections. However, given significant interpatient variability in its pharmacokinetics, maintaining plasma concentrations is difficult within its characteristically narrow therapeutic window. This is especially challenging in patients with unstable renal function. Thus, the aim of this study was to develop a population pharmacokinetic model for vancomycin that is suitable for Thai patients with variable renal functions, including those with unstable renal function.

METHODS

Data from 213 patients, including 564 blood samples, were retrospectively collected; approximately 70% patients exhibited unstable renal function during vancomycin treatment. The model building group was randomly assigned 108 patients and the remaining 33 patients comprised the validation group. A population pharmacokinetic model was developed that incorporated drug clearance (CL) as a function of time-varying creatine clearance (CrCL). The predictive ability of the resulting population model was evaluated using the validation data set, including its ability to forecast serum concentrations within a Bayesian feedback algorithm.

RESULTS

A 2-compartment model with drug CL values that changed with time-varying CrCL adequately described vancomycin pharmacokinetics in the evaluated heterogeneous patient population with unstable renal function. Vancomycin CL was related to time-varying CrCL as follows: CL (t) = 0.11 + 0.021 × CrCL (t) (CrCL <120 mL/min. Using the population model, Bayesian estimation with at least one measured serum concentration resulted in a forecasting error of small bias (-2.4%) and adequate precision (31.5%).

CONCLUSIONS

In hospitals with a high incidence of unstable renal function, incorporating time-varying CrCL with Bayesian estimation and at least one measured drug concentration, along with frequent CrCL monitoring, improves the predictive performance of therapeutic drug monitoring of vancomycin.